gms | German Medical Science

Aim: Dosing recommendations from guidelines are indisputable. They promote the safety of a medical treatment and enable standardisation of pharmacotherapy amongst hospitals. Nevertheless, guidelines most often ignore the heterogeneity of patients and possible factors that influence the pharmacokinetics (PK) and pharmacodynamics (PD) of a drug. As a result, individualised dosing recommendations are most often empirically derived and based on experience of the clinician only. To introduce evidence- based dose individualisation and to support guideline recommendations, mathematical PK/PD modelling can be a helpful tool. The advantages of this approach will be illustrated by three different examples.

Method: All three examples are based on the population PK/PD approach using NONMEM^® and R^®.

Results: Renal impairment highly influences the PK of renally excreted drugs, which is most often reflected in a recommendation for dose adjustment in patients with renal dysfunction within guidelines. When no data from a dedicated renal impairment trial is available, mathematical modelling can become an important part of dosing recommendations in guidelines. To illustrate the impact the case of the proposed 75 mg twice-daily dose for dabigatran etexilate in atrial fibrillation patients with low creatinine clearance (15–30 mL/min) [1] will be explained.

For patients with community-acquired pneumonia (CAP) fluorchinolones are a common treatment option. The CAP guideline for levofloxacin advises a daily dose of 500–1000 mg depending on the severity of the disease [2]. However, the current guideline lacks dose individualisation based on known factors influencing the PK of the drug. To close this gap the results of a population PK/PD analysis for levofloxacin will be presented, which enables dose individualisations based on renal function and different underlying diseases.

Most often results of population PK/PD are not easily transferable into clinical practice, which partly explains why this useful approach for dose recommendations often fails. To enable easier communication of population PK/PD results and promote usage of this approach the web-based “Shiny” tool in R^® for a selected anti-infective drug will be introduced.

Conclusion: In conclusion, mathematical modelling is an interesting tool to complement dosing recommendations from guidelines and improve dose individualisation in clinical practice.